Farm Level Assessment of Irrigation Performance for Dairy Pastures in the Goulburn-Murray District of Australia by Combining Satellite-Based Measures with Weather and Water Delivery Information

Abstract

Pasture performance of 924 dairy farms in a major irrigation district of Australia was investigated for their water use and water productivity during the 2015-2016 summer which was the peak irrigation period. Using satellite images from Landsat-8 and Sentinel-2, estimates of crop coefficient (Kc) were determined on the basis of a strong linear relationship between crop evapotranspiration (ETc) and vegetation index (NDVI) of pasture in the region. Utilizing estimates of Kc and crop water requirement (CWR), NDVI-dependent estimates of Irrigation Water Requirement (IWR) were derived based on the soil water balance model. In combination with daily weather information and seasonal irrigation water supply records, IWR was the key component in the understanding of current irrigation status at farm level, and deriving two irrigation performance indicators: (1) Relative Irrigation Water Use (RIWU) and (2) Total Irrigation Water Productivity (TIWP). A slightly higher proportion of farm irrigators were found to be either matching the irrigation requirement or under-watering (RIWU ≤ 1.0). According to TIWP, a few dairy farms (3%) were found to be in the category of high yield potential with excess water use, and very few (1%) in the category of limited water supply to pastures of high yield potential. A relatively high number of farms were found to be in the category where excess water was supplied to pastures of low-medium yield potential (27%), and farms where water supply compromised pastures with a sub-maximal vegetation status (15%). The results of this study will assist in objectively identifying where significant improvement in efficient irrigation water use can be achieved.

Performance assessment and indicators for agricultural water management—A review

“…2). The PIs were used in different PAs (details in Section 4.2) and have played a significant role in water allocation, in that many have used indicators to assess how water is distributed between small farm holders (Abou Kheira, 2009;Abuzar et al, 2017;Agam et al, 2013;Ahadi et al, 2013;Awan et al, 2011;Awulachew & Ayana, 2011;Sánchez et al, 2015a;Setegn et al, 2011), upstream versus downstream farmers (Ahmad et al, 2017;Shanono et al, 2014). Table 2 Although many PIs were reviewed, this section is not intended to discuss all the PIs, but merely a summary of the first nine most frequently used PIs including their formulae.…”

Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy

“…Crop coefficients are then inputted, along with meteorological data, to soil water balance models to estimate rates of irrigation given assumptions about the level of soil moisture depletion at which irrigation will be triggered and expected application and conveyance efficiencies of water use. A key difference between crop coefficient and thermal‐infrared or soil moisture‐based models is that reflectance‐based crop coefficient models are most commonly used to provide estimates of crop irrigation requirements rather than actual abstraction rates (Abuzar et al, 2017; Campos et al, 2017; Foster et al, 2019; Gonçalves et al, 2020; Santos et al, 2010; Segovia‐Cardozo et al, 2019; Vuolo et al, 2015). This is because reflectance‐based crop coefficients capture reductions in crop ET caused by suboptimal crop development over the growing season but do not provide direct information about additional reductions in crop ET as a result of water stress limiting plant transpiration.…”

“…In practice, these assumptions are unlikely to capture the true heterogeneity in field or farmer level irrigation efficiency. Several studies in our sample estimate crop irrigation requirements using either thermal‐infrared or crop coefficient models and compare these estimates to in situ abstraction data (Abuzar et al, 2017; Campos et al, 2017; Elnmer et al, 2018; Foster et al, 2019; Gonçalves et al, 2020; Ma et al, 2018; Santos et al, 2010; Segovia‐Cardozo et al, 2019; Vuolo et al, 2015; Wu et al, 2015). Universally, these studies show that efficiency—defined here as the ratio of irrigation abstracted or applied to crop irrigation requirements—varies significantly both between fields and over time.…”

Assessing Suitability of Sentinel-2 Bands for Monitoring of Nutrient Concentration of Pastures with a Range of Species Compositions

“…Specifications of Sentinel-2 include 13 multispectral bands including three novel red-edge bands for vegetation monitoring, high spatial resolution (10,20 and 60 m), short revisit time (~5 days using two satellites), large swath width, higher signal to noise ratio, as well as the data being freely available. Sentinel-2 data have already been assessed for irrigation performance for dairy pastures [19], shown promise in quantifying biomass response to different fertilizer treatments [17] and have been found effective for retrieval of pasture structural parameters such as Leaf Area Index (LAI), which provides information about pasture canopy growth and density [18]. On the other hand, the potential of Sentinel-2 bands for monitoring pasture quality parameters relating to nutritional content has not been tested widely.…”